From denial-of-service attacks to spreading of ransomware or other malware across an organization's network, it is possible that manually operated defenses are not able to respond in real time at the scale required, and when a breach is detected and remediated the damage is already made. Autonomous cyber defenses therefore become essential to mitigate the risk of successful attacks and their damage, especially when the response time, effort and accuracy required in those defenses is impractical or impossible through defenses operated exclusively by humans. Autonomous agents have the potential to use ML with large amounts of data about known cyberattacks as input, in order to learn patterns and predict characteristics of future attacks. Moreover, learning from past and present attacks enable defenses to adapt to new threats that share characteristics with previous attacks. On the other hand, autonomous cyber defenses introduce risks of unintended harm. Actions arising from autonomous defense agents may have harmful consequences of functional, safety, security, ethical, or moral nature. Here we focus on machine learning training, algorithmic feedback, and algorithmic constraints, with the aim of motivating a discussion on achieving trust in autonomous cyber defenses.
翻译:从拒绝服务攻击到在整个组织网络传播赎金软件或其他恶意软件,手工操作的防御可能无法按要求的规模实时作出反应,而且一旦发现并补救了损坏,自主网络防御因此成为减轻攻击成功风险及其损害的关键,特别是当这些防御所需的反应时间、努力和准确性不切实际或不可能通过完全由人操作的防御来减少时。自主代理有可能使用大量已知网络攻击的数据作为投入,以便了解未来攻击的模式和预测特征。此外,从过去和目前的攻击中学习能够使防御适应与以往攻击具有相同特点的新威胁。另一方面,自主网络防御带来意外伤害的风险。自主防御代理的行动可能产生功能、安全、安保、道德或道德性质的有害后果。我们在这里侧重于机器学习培训、算法反馈和算法限制,目的是激发对自主网络防御的信任。